Optionally, a double peaked Ly line profile can be fitted as the sum of two skewed Gaussians.
The program uses the front-end lmfit minimization packagelmfit.github.io/lmfit-py [1]} based on scipy optimize nonlinear least-squares minimization [2]}. The default minimization routine is the trust region reflective least squares algorithm, which is more robust and gives a better error estimate than the classical Levenberg–Marquardt algorithm. Other minimization algorithms can be selected (see the lmfit documentation for details). The Markov chain Monte Carlo emcee package [3]} is also used to improve error computation.
Information about the public released version of the software and its documentation is given in Sects. REF  and .
